Databases are collections of data, so all the data visualization options can be used for databases
Reports (next week) are commonly used and will frequently have images and summaries
Analytics and business intelligence are common and will use a lot of visuals
Visualizations
Showing database relationships and how the schema works
Showing portions of the data and query results
Visualize query or database performance
Types of visualizations:Charts, maps, graphs, live widgets, and dashboards
Auto and manual visualizations - is the visualization made by a person, or automatically done by the program/database/tool
AI based visualizations - AI is getting used for a lot of art and visualizations, however the caveats and warnings for all AI still apply here, including ethics
Example: Charts and Graphs
Charts are the overall term for data visuals
Charts are used to show large amounts of data in a way where patterns are more clear to see
All graphs are charts, not all charts are graphs
Graphs are usually used to show raw data trends over time
Line and bar graphs are two really common options
Example: Dashboards and Widgets
Dashboards are how we can visualize what's going on with information
Widgets are the things that make up the dashboard
Each widget is designed to show some piece of info
Widgets should be kept to key metrics, don't add widgets unless you are sure of their value
Widgets can have different refresh rates depending on how important it is to have up to date data
How to tell if it's a good visualization
Scalability - How well it works with larger amounts of data
Readability - How well you can understand what the visualization is trying to communicate
Useability - Is this data something we need, and does this illustrate that well
Interactivity - Is this visualization one we can change on the fly, or is it a static visual
Aesthetics - How pretty is it
Accessibility - This can be a tough thing to do because there can be a wide variety of things that keeps something inaccessible. Some questions we can ask are
Visualizations that mislead the viewer, either on purpose or by accident
Hiding relevant data, or inaccurately representing data by changing things like scale and proportion, where the chart starts/ends are usually trying to falsely lead you somewhere
Showing too much data to confuse the viewer either obviously like a lot of 3D graphs, or more subtly to give the impression of well thought out analysis when in reality it's trying to hide things
Lack of context, labels, or any way to tell what the visualization is about and why it was made
Using the right data, but in confusing ways to try and lead the viewer into thinking you're saying one thing but you really mean another
One common issue with visualizations is showing data in ways that are misrepresentations, or willfully misleading
For example, if the average salary is €2000 (pretend that's good) then you might think yay people make good money, but if you look at the numbers, 9 of 10 people make €1000 and one person made €10,000 euro you'd see it's actually not great and misrepresents the affluency of the area
Let's say we're looking at response time, the average makes it look faster than it really is, the median makes it seem closer to reasonable, but it doesn't show the very fast but very failed transactions
Averages can be useful, but you need to make sure they are accurately representing your data
Example: Percentiles
Percentiles will show more accurate information in some cases because they include how much of the data is represented by the visualization
"To calculate the 10th percentile, let's say we have 10,000 values. We take all of the values, order them from smallest to largest, and identify the 1001st value (where 1000 or 10% of the values are below it), which will be our 10th percentile" from this blog on percentiles vs averages
Having this insight can allow a company to figure out there are problems faster, and respond faster
Companies don't like lots of tickets, or long wait times on tickets, predicting issues and seeing them quickly can mitigate those
Data visualizations can be tough because of everything from labelling issues, to colour or colour contrast, to lack of alt text
Accessibility should be baked in to what you're doing, not seen as an after thought
Keeping your visualizations simple can help because they can be easier to describe and offer alternates for
Being mindful of colours and contrast is helpful for both text and images. If someone can't see colours, does your visualization still convey your meaning? If not, can you change it so it does?
Think about offering different formats so that it's easier for everyone to understand what you're trying to share